Recently, rapid growth of the wireless mobile communication market requires various multimedia services in a wireless environment. Particularly, massive transmit data and high-speed data delivery are in progress. In this respect, the most urgent task is to find a method for efficiently utilizing limited frequencies. For doing so, a new transmission technology using multiple antennas is demanded (for example, a Multiple-Input Multiple-Output (MIMO) system using multiple antennas).
The MIMO technique employs the multiple antennas at the transmitter and the receiver. Compared to a Single-Input Single-Output (SISO) system, the MIMO system can increase a channel transmission capacity in proportion to the number of the antennas without requiring additional frequencies or transmit power allocation, on which researches are conducted.
The multi-antenna techniques are largely divided into a spatial diversity scheme which enhances a transmission reliability by obtaining a diversity gain corresponding to the product of the numbers of the transmit antennas and the receive antennas, a Spatial Multiplexing (SM) scheme which raises the data rate by transmitting a plurality of signal streams at the same time, and a scheme incorporating the spatial diversity and the multiplexing.
When transmitters send different data streams using the SM scheme of the multi-antenna technology, interference occurs between the transmitted data. The receiver detects the signal using a Maximum Likelihood (ML) receiver with consideration to the influence of the interference signal, or detects the signal after canceling the interference. The interference cancellation methods include Zero Forcing, Minimum Mean Square Error (MMSE), and so on. In the general SM scheme, the performance of the receiver is in a tradeoff relation with the computational complexity of the receiver. Researches are conducted on reception algorithms for lowering the computational complexity of the receiver and achieving performance close to the ML receiver.
The multi-antenna system transmits and receives data via the multiple antennas of the transmitter and the receiver. For example, using n-ary transmit antennas and m-ary receive antennas, the receive signal can be expressed as Equation 1:
                    y        =                                                            H                X                            +              u                        ⇒                          [                                                                                          y                      1                                                                                                                                  y                      2                                                                                                            ⋮                                                                                                              y                      m                                                                                  ]                                =                                                    [                                                                                                    h                        11                                                                                                            h                        12                                                                                    …                                                                                      h                                                  1                          ⁢                                                                                                          ⁢                          n                                                                                                                                                                        h                        21                                                                                                            h                        22                                                                                    …                                                                                      h                                                  2                          ⁢                                                                                                          ⁢                          n                                                                                                                                                ⋮                                                              ⋮                                                              ⋮                                                              ⋮                                                                                                                          h                                                  m                          ⁢                                                                                                          ⁢                          1                                                                                                                                    h                                                  m                          ⁢                                                                                                          ⁢                          2                                                                                                            …                                                                                      h                        mn                                                                                            ]                            ⁡                              [                                                                                                    x                        1                                                                                                                                                x                        2                                                                                                                        ⋮                                                                                                                          x                        n                                                                                            ]                                      +                                          [                                                                                                    u                        1                                                                                                                                                u                        2                                                                                                                        ⋮                                                                                                                          u                        m                                                                                            ]                            .                                                          [                  Eqn          .                                          ⁢          1                ]            
In Equation 1, y denotes a receive signal vector, yi denotes the receive signal at the i-th receive antenna, H denotes a channel matrix, and hij denotes a channel gain between the j-th transmit antenna and the i-th receive antenna.
X denotes a transmit symbol vector, xj denotes the transmit symbol via the j-th transmit antenna, u denotes a background noise, and ui denotes a background noise of the i-th receive antenna.
The receiver can perform ML detection to acquire optimal reception performance with the receive signal based on Equation 2. ML detection greatly enhances performance by calculating Euclidean distances and selecting a symbol vector having the shortest straight distance.
                              x          ^                =                                            arg              ⁢                                                          ⁢              min                                      x              ∈              A                                ⁢                                                                                      y                  -                  Hx                                                            2                        .                                              [                  Eqn          .                                          ⁢          2                ]            
In Equation 2, A denotes every possible candidate symbol vector set of the transmit symbol vector X. Given a modulation order M, A includes Mn-ary candidate symbol vectors in total.
Disadvantageously, as the modulation order of the transmit symbol and the number of the transmit antennas increase, the computations in the ML detection exponentially increase.
Meanwhile, Equation 2 pertains to ML detection according to a hard decision without considering a channel decoder. With respect to the channel decoder, it is necessary to calculate a Log Likelihood Ratio (LLR) for each data bit based on Equation 3:
                                                                        L                ⁡                                  (                                                                                    b                        λ                                            |                      y                                        ,                    H                                    )                                            =                            ⁢                              ln                ⁢                                                      Pr                    ⁡                                          (                                                                                                    b                            λ                                                    =                                                      0                            |                            y                                                                          ,                        H                                            )                                                                            Pr                    ⁡                                          (                                                                                                    b                            λ                                                    =                                                      1                            |                            y                                                                          ,                        H                                            )                                                                                                                                              =                            ⁢                              ln                ⁢                                                                            ∑                                                                        x                          λ                                                ∈                                                  A                                                      λ                            =                            0                                                                                                                                                                                    ⁢                                          exp                      ⁡                                              (                                                  -                                                                                                                                                                                      y                                  -                                                                      Hx                                    λ                                                                                                                                                              2                                                                                      σ                              u                              2                                                                                                      )                                                                                                                        ∑                                                                        x                          λ                                                ∈                                                  A                                                      λ                            =                            1                                                                                                                                                                                    ⁢                                          exp                      ⁡                                              (                                                  -                                                                                                                                                                                      y                                  -                                                                      Hx                                    λ                                                                                                                                                              2                                                                                      σ                              u                              2                                                                                                      )                                                                                                                                                                    ≈                            ⁢                                                                                                                  min                                                                              x                            λ                                                    ∈                                                      A                                                          λ                              =                              1                                                                                                                          ⁢                                                                                                                              y                            -                                                          Hx                              λ                                                                                                                                2                                                              -                                                                  min                                                                              x                            λ                                                    ∈                                                      A                                                          λ                              =                              0                                                                                                                          ⁢                                                                                                                              y                            -                                                          Hx                              λ                                                                                                                                2                                                                                                  σ                    u                    2                                                  .                                                                        [                  Eqn          .                                          ⁢          3                ]            
To calculate the LLRs based on Equation 3, it is necessary to calculate symbol vectors corresponding to cases according to values of the bits of each symbol. In so doing, complicated detection is required.
To address this problem, a Parallel Detection (PD) is utilized.
FIGS. 1A and 2B depict candidate symbols of ML detection and a PD scheme of a general receiving apparatus.
To explain the differences between ML detection and the PD scheme, the receiving apparatus is assumed to include two transmit antennas and two receive antennas and to adopt a Quadrature Phase Shift Keying (QPSK) modulation.
FIG. 1A depicts the candidate symbols according to ML detection of the general receiving apparatus.
Referring to FIG. 1A, ML detection of the receiving apparatus uses every combination of the symbols x1 and x2 transmitted via the transmit antennas with the candidate symbols and generates sixteen (=4×4) candidate symbol vectors in total.
FIG. 1B depicts the candidate symbols according to the PD scheme of the general receiving apparatus.
Referring to FIG. 1B, the receiving apparatus selects one of the symbols transmitted via the two transmit antennas, cancels interference in the receive signal in every possible case of the symbol, and determines the other symbol through slicing. Thus, the number of the candidate symbol vectors used in the receiving apparatus is reduced to four. As such, the PD scheme can lower the complexity of the apparatus in calculating the Euclidean distance.
The PD scheme of the receiving apparatus is now described.
The receiving apparatus selects one (e.g., x1) of the two transmit symbols (e.g., x1 and x2) and cancels interference in every candidate symbol of the selected symbol (e.g., x1).
Next, the receiving apparatus determines a candidate symbol value of the other transmit signal symbol (e.g., x2) using the receive signal changed through the interference cancellation.
The above-mentioned PD scheme features a lower complexity than ML detection in calculating the Euclidean distance with similar performance to ML detection. Disadvantageously, without considering channel decoding, when channel decoding is actually required, abnormal operations result in the LLR calculation.
FIG. 2 depicts shortcomings in the PD scheme of the general receiving apparatus.
More particularly, FIG. 2 shows the remaining candidate symbol values of the other transmit signal symbol (e.g., x2) after the PD of the receiving apparatus.
The candidate symbol values are divided to right and left according to their first bit of x2 along the y axis. The receiving apparatus needs to find the shortest Euclidean distance in the case 203 where the first bit is 0 and in the case 201 where the first bit is 1. Since there are no candidate symbol values for the first bit ‘1’ as shown in FIG. 2, abnormal operations result in the LLR calculation.
To address this abnormal operation, a Modified ML (MML) (Korean Application Publication No. 10-2007-0052037 titled “APPARATUS AND METHOD FOR GENERATING LLR IN MIMO COMMUNICATION SYSTEM”) can be used. However, since every candidate symbol value is used for the symbols including the corresponding bit to avoid the abnormal operation, Euclidean distances are computed for M-ary candidate symbol vectors. As a result, as the modulation order increases, the computational complexity is still problematic.